Custom planning & data tools, built to fit your operation.

Problem

Labelling time-series data is slow and inconsistent: analysts tag demand spikes, promotions and anomalies by hand, and their reasoning stays in their heads.

Approach

ChronoTag suggests labels with ML. Users confirm or adjust them in a purpose-built interface, and every decision is recorded, so the models keep improving.

Result

One central, reusable source of truth for labelled data: faster and more consistent tagging, with an audit trail of who decided what. The demo runs on realistic sales history across many SKUs.

Problem

Scheduling cleaning crews across dozens of short-stay properties by hand takes hours every day, and still misses check-in deadlines.

Approach

SparkSched generates the day's schedule at the press of a button, using optimization that accounts for deadlines, start times, travel and agent availability.

Result

In the demo: 134 tasks across 21 agents planned within a 7.5-hour window, zero past deadline.